Artificial Intelligence and Precision Oncology: Bridging Cancer Research and Clinical Decision Support

Preface
Contents
Editor and Contributors
Chapter 1: The Application of AI in Precision Oncology: Tailoring Diagnosis, Treatment, and the Monitoring of Disease Progress...
	1.1 Introduction
	1.2 AI in Medicine
	1.3 Biomarker Discovery and Application
	1.4 Multi-omics Data
		1.4.1 Genomics
		1.4.2 Transcriptomics
		1.4.3 Proteomics
		1.4.4 Metabolomics
		1.4.5 Microbiomics
	1.5 Imaging
		1.5.1 Radiogenomics
	1.6 Drugs, AI and Precision Oncology
		1.6.1 Drug Discovery and Re-purposing
		1.6.2 Digital Twins
	1.7 Conclusion
	References
Part I: Artificial Intelligence for Screening, Diagnosis, Monitoring in Precision Oncology
	Chapter 2: Application of AI in Novel Biomarkers Detection that Induces Drug Resistance, Enhance Treatment Regimens, and Advan...
		2.1 Introduction
		2.2 AI Advances in Healthcare and Precision Oncology
		2.3 Classification of Biomarkers
		2.4 Oncology Biomarkers: Solid Biomarkers vs Liquid Biomarkers
		2.5 Advances in Biomarker Discovery: Liquid Biopsies
		2.6 AI in Cancer Biomarker Discovery
		2.7 AI in the Detection of Novel Biomarkers for Accurate Prognostication and Prediction of Drug Resistance to Enhance Treatment
		2.8 Challenges, Limitations, and Opportunities
		2.9 Conclusions and Perspectives
		References
	Chapter 3: Use of Artificial Intelligence in Implementing Mainstream Precision Medicine to Improve Traditional Symptom-driven ...
		3.1 Introduction
		3.2 Artificial Intelligence
		3.3 Use of Artificial Intelligence for Early Interventions and Tailoring Better-personalised Treatment of Common Cancers
			3.3.1 Breast Cancer
			3.3.2 Colorectal Cancer
			3.3.3 Lung Cancer
			3.3.4 Cancer of the Cervix
			3.3.5 Gastric Cancer
			3.3.6 Prostate Cancer
			3.3.7 Malignant Melanoma
			3.3.8 Ovarian Cancer
			3.3.9 Hepatocellular Carcinoma
				3.3.9.1 Carcinoma of Oesophagus
			3.3.10 Pancreatic Adenocarcinoma
			3.3.11 Other Cancers
		3.4 Limitations
		3.5 Conclusion
		References
	Chapter 4: AI as a Novel Approach for Exploring ccfNAs in Personalized Clinical Diagnosis and Prognosis: Providing Insight int...
		4.1 Introduction
		4.2 Cancer Liquid Biopsies and Their Use in Precision Oncology
			4.2.1 Cell-Free DNAs
			4.2.2 Circulating Tumor DNA
			4.2.3 Cell-free Mitochondrial DNA in Cancer
		4.3 AI and Cell-free RNAs in Cancer
			4.3.1 Non-coding RNA in Cancer
			4.3.2 Long Non-coding RNA in Cancer
			4.3.3 The Role of MicroRNAs in Human Cancer
			4.3.4 Gene Silencing in Diagnosis and Prognosis of Cancer
		4.4 Limitations and Future Perspectives
		4.5 Conclusion
		References
	Chapter 5: AI-Enhanced Digital Pathology and Radiogenomics in Precision Oncology
		5.1 Introduction
		5.2 Medical Imaging in Precision Medicine
			5.2.1 Magnetic Resonance Imaging (MRI) in Precision Medicine
			5.2.2 Computed Tomography (CT) Scan in Precision Medicine
			5.2.3 Positron Emission Tomography (PET)/Computed Tomography (CT) in Precision Medicine
			5.2.4 CT vs. PET/CT Comparisons: The Preferred Choice
		5.3 Digital Pathology and AI
			5.3.1 Reporting the Results
		5.4 Radiogenomics and Artificial Intelligence and Its Use in Precision Medicine
			5.4.1 Acquisition of Raw Images
			5.4.2 Pre-processing of Information
			5.4.3 Extraction of Features
			5.4.4 Data Analysis
			5.4.5 Current Application of Radiogenomics in Oncology
		5.5 Limitations
		5.6 Conclusion
		References
Part II: Artificial Intelligence and Omics in Precision Oncology
	Chapter 6: Epigenetics Analysis Using Artificial Intelligence in the Era of Precision Oncology
		6.1 Introduction
		6.2 Types of Epigenetic Modifications
			6.2.1 DNA Methylation
			6.2.2 RNA Regulation
			6.2.3 Histone Modifications
			6.2.4 Chromosomal Structure
		6.3 AI in the Analysis of Epigenomics
			6.3.1 Supervised Learning
			6.3.2 Unsupervised Learning
			6.3.3 Deep Learning
		6.4 The Practical Use of Epigenetic Data and AI in the Management of Cancer
		6.5 Limitations of AI-Driven Epigenomics Applications
		6.6 Conclusions
		References
	Chapter 7: Association of Metabolomics with AI in Precision Oncology: Emerging Perspectives for More Effective Cancer Care
		7.1 Definitions and Broad Applications
			7.1.1 Metabolomics
			7.1.2 Analytical Techniques in Metabolomics
			7.1.3 Limitations of Metabolomics
		7.2 Precision Oncology
		7.3 Artificial Intelligence
		7.4 Cancer Management and AI
			7.4.1 Diagnosis and Treatment of Cancer
			7.4.2 Biomarkers
			7.4.3 Challenges Facing the Application of AI to Cancer Diagnosis, Prognosis and Treatment
		7.5 The Solution to the Challenges of AI Applications
		7.6 Precision Medicine in Cancer Care
			7.6.1 Introduction
			7.6.2 A Summary of the Application of AI to Precision Medicine
		7.7 Application of AI to Metabolomics
			7.7.1 Application in Therapeutics
		7.8 Application in Imaging Genomics (Radiomics/Radiogenomics)
		7.9 The Future of Cancer Care
		7.10 Conclusion
		References
	Chapter 8: Artificial Intelligence Application to Microbiomics Data for Improved Clinical Decision Making in Precision Oncology
		8.1 Introduction
		8.2 The Human Microbiome
		8.3 The Microbiome and Cancer
		8.4 Microbiomics
			8.4.1 Techniques in Microbiomics
				8.4.1.1 Quantitative Microbial Profiling Methods
				8.4.1.2 Multi-omics Technologies
		8.5 Artificial Intelligence: Big Data and Machine Learning
			8.5.1 Big Data
			8.5.2 Machine Learning in Microbiomics
		8.6 Advancing Precision Oncology
		8.7 Targeting the Microbiome in the Treatment of Cancer
		8.8 Limitations
		8.9 Conclusions
		References
Part III: Artificial Intelligence in Cancer Therapy and Clinical Applications
	Chapter 9: AI and Nanomedicine in Realizing the Goal of Precision Medicine: Tailoring the Best Treatment for Personalized Canc...
		9.1 Introduction
		9.2 Nanotechnology Solutions in Precision Medicine
			9.2.1 Combining AI and Nanotechnology Solutions in Tailoring the Best Treatment for Cancer Treatment
		9.3 Role of AI in Drug Development Optimization
			9.3.1 Role of Artificial Intelligence in Clinical Therapy: Drug Dosing and Therapeutic Efficacy Correlation
			9.3.2 Role of AI in Improved Targeting
			9.3.3 Role of AI in Gene Therapy
		9.4 Challenges with AI Integrated Nanotechnologies
			9.4.1 AI-Enabled Nanomedicine
			9.4.2 Current Nanotechnology Strategies
		9.5 Conclusion and Perspectives
		References
	Chapter 10: Artificial Intelligence-Based Medical Devices Revolution in Cancer Screening: Impact into Clinical Practice
		10.1 Introduction
		10.2 The Definition and Characteristics of an AI Device
		10.3 History of Artificial Intelligence (AI) Devices
		10.4 The Basis of AIMDs
		10.5 The Practical Use of AI Devices in Cancers
			10.5.1 Radiology and the Analysis of Images for Pathology
			10.5.2 Endoscopy
		10.6 The Regulation of AI-based Devices
		10.7 Drawbacks and Limitations of AI Devices
		10.8 Conclusion and Future Perspectives
		References
	Chapter 11: Intelligent Drug Design and Use for Cancer Treatment: The Roles of AI and Precision Oncology in Targeting Patient-...
		11.1 Introduction
		11.2 The Application of AI in Drug Design
		11.3 The Role of AI in Drug Screening
			11.3.1 Prediction of Physicochemical Properties and Bioactivity Using AI
			11.3.2 AI Predictions of the Mode of Action of Potential Drugs
		11.4 Techniques and Tools for Computational Drug Discovery
		11.5 Protein Modelling and Docking
		11.6 Drugs Targeting Alternative Splicing
		11.7 Other Applications of AI in Drug Design
		11.8 Limitations to AI-Based Drug Design
		11.9 Conclusion
		References
	Chapter 12: Applying Artificial Intelligence Prediction Tools for Advancing Precision Oncology in Immunotherapy: Future Perspe...
		12.1 Introduction
			12.1.1 Cancer Immunotherapy
				12.1.1.1 The Efficacy of Cancer Immunotherapy
			12.1.2 AI and Biomarker Prediction Tools
				12.1.2.1 Identification of Genomic Immune Signatures
				12.1.2.2 Long Noncoding RNAs as Prognostic Markers
				12.1.2.3 MicroRNAs as Prognostic Markers
				12.1.2.4 Radiomics as Therapeutic Response Monitoring Tools
				12.1.2.5 Other Approaches
		12.2 Integration of AI Tools in the Enhancement of Cancer Immunotherapies
			12.2.1 AI Tools for the Prediction of Novel Immune-Related Adverse Events
			12.2.2 Implementation of AI Tools for Monitoring Patient Compliance to Cancer Immunotherapy
		12.3 Challenges of AI in Cancer Immunotherapy
		12.4 Future Perspectives
		12.5 Conclusion
		References
	Chapter 13: Employing AI-Powered Decision Support Systems in Recommending the Most Effective Therapeutic Approaches for Indivi...
		13.1 Introduction
		13.2 AI-Tools in Optimising Drug Combinations and Enhancing Effective Cancer Therapeutics: From Drug Development to Personalis...
		13.3 AI-Empowered Clinical Decision Support: Applications in Chemotherapy, Radiotherapy, Immunotherapy
		13.4 AI-Enabled Adaptive Cancer Therapy
		13.5 Challenges and Limitations
		13.6 Conclusions
		References
	Chapter 14: AI-Pathway Companion in Clinical Decision Support: Enabling Personalized and Standardized Care Along Care Pathways...
		14.1 Clinical Decision Support Systems
			14.1.1 Defining Clinical Decision Support Systems (CDSS)
			14.1.2 Clinical Decision Support Systems in Clinical Practice and Clinical Trials
			14.1.3 Clinical Decision Support Systems Feature in Clinical Practice
			14.1.4 Clinical Decision Support Systems in Pathology
		14.2 AI-Pathway Companion in CDSS: Cancer Control and Prevention Includes Awareness, Screening and Early Diagnosis
			14.2.1 AI-Pathway Companion in Clinical Decision Support
			14.2.2 What Is an AI-Pathway Companion?
			14.2.3 AI-Pathway Prostate Cancer
			14.2.4 AI-Pathway Companion Breast Cancer
			14.2.5 AI-Pathway Companion Coronary Artery Disease
			14.2.6 AI-Pathway Companion Infectious Diseases
		14.3 Clinical Uses of AI-Pathway Companion
		14.4 Cancer Prevention and Control Using COMPAS
		14.5 Overview of Clinical Decision Support Systems
		14.6 Machine Learning Tools
		14.7 Predictive Models Assist in Decision-making
		14.8 Developing Treatment Responses
		14.9 Limitations in the Application of AI in Precision Medicine
		14.10 Conclusion
		References
	Chapter 15: AI Tools Offering Cancer Clinical Applications for Risk Predictor, Early Detection, Diagnosis, and Accurate Progno...
		15.1 Introduction
		15.2 AI-based Tools in Clinical Oncology Workflows
		15.3 AI-Models for Predicting Clinically Relevant Parameters in Advancing Precision Oncology
		15.4 AI-Enhanced Technologies in Early Cancer Detection, Diagnosis, Risk Stratification and Prognosis
		15.5 AI from Bench to Bedside: Challenges and Limitations
		15.6 Conclusions
		References
	Chapter 16: Conclusion and Insights into the Future of AI in Precision Oncology
		16.1 Conclusion